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Orchestration Audit: S1-df546b88

Session Metadata

Transcript: /home/jared/.claude/projects/-home-jared-dev-cc-os/df546b88-e27d-47e1-888c-648012c24e62.jsonl
cwd: /home/jared/dev/cc-os
Duration: 2026-07-06 15:32:42 to 18:20:38 (2h 48m)
Assistant turns: 66 (all Fable-5 / Haiku 4.5)
Agent spawns: 10 (general-purpose ×3, perspectives ×4, Explore ×1, general-purpose ×2)
Model params: All 10 requested model: "sonnet" → all 10 resolved as claude-haiku-4-5-20251001

Critical Finding

Agent model parameter is not being respected by the Agent framework. All 10 agents explicitly requested model: "sonnet" in their tool_use input but resolved to Haiku. This prevents the orchestrator from implementing the cost-quality tradeoff specified in ORCHESTRATION.md.

Evidence: Line 21 (agent spawn 1) shows "model": "sonnet" in the Agent input; line 22 (toolUseResult) shows "resolvedModel": "claude-haiku-4-5-20251001". Pattern holds for all 10 agents (lines 21, 23, 25, 79, 81, 83, 85, 202, 218, 219 as spawns; each followed by a tool result line with resolvedModel=haiku).

Q1: Are subagents getting called when they should be?

Verdict: PASS (with user's explicit request caveat)

The user's initial prompt explicitly requests delegation: "Dispatch subagents based on model to intelligence and cost (low cost, high quality) to explore and research options, approaches as well as tools and services."

Evidence:

Line 4 (user prompt): "Dispatch subagents based on model to intelligence and cost (low cost, high quality)"
Lines 20-21: Orchestrator accepts the delegation request and clarifies reasoning: "Dispatching three sonnet researchers now (this is judgment-heavy evaluation work, not mechanical, so no haiku)"
Agents 1-3 (lines 21, 23, 25) research distinct tools (Storybloq, self-hosted kanban options, agent-native backlogs) — parallelizable research tasks with no dependencies

Pattern observation: Agents 4-7 (perspectives) and 8-10 (deep-dives) appear to be reactive rather than pre-planned, running after earlier results come back. This is defensible (each synthesizes prior findings) but weakens the "pre-planned delegation" story.

Candidate issue: Agents 8-10 (Explore Hermes, Planka deep-dive, modern kanban alternatives sweep) lack clear pre-planning signals in the transcript. They appear reactive to synthesized agent findings rather than pre-delegated work.

Q2: Is the correct model chosen per subagent?

Verdict: FAIL — Model parameter ignored by Agent framework

The orchestrator correctly specifies model parameters per ORCHESTRATION.md policy:

All 10 agents requested model: "sonnet" (judgment-heavy research and perspective work)
Policy expectation: Sonnet for judgment work, Haiku for mechanical work
Actual outcome: All 10 resolved to Haiku

Evidence:

Line 21: "model": "sonnet" in Agent tool input
Line 22: "resolvedModel": "claude-haiku-4-5-20251001" in toolUseResult
Line 23: Same pattern; line 24: "resolvedModel": "claude-haiku-4-5-20251001"
Line 25: "model": "sonnet"; line 26: "resolvedModel": "claude-haiku-4-5-20251001"
Agents 4-7 (perspectives: devils-advocate, simplifier, implementer, premortem): all requested model: "sonnet" but resolved to Haiku
Agents 8-10: same pattern (lines 202, 218, 219 spawns → Haiku resolves on subsequent lines)

Root cause: Unknown — likely Agent framework / Claude Code plugin issue, not orchestrator error. The orchestrator is making the correct specification; the framework is downgrading.

Impact: Orchestrator cannot implement the stated policy of "sonnet for judgment work, haiku for mechanical work." All work runs at lower quality/cost than specified.

Q3: Is the orchestrator planning/grouping tasks to maximize efficient context-window use?

Verdict: MIXED — Good initial batching, then reactive pattern

Initial parallelization (agents 1-3):

Agents 1-3 dispatched together to research three distinct but related kanban systems
Independent research tasks with no inter-dependencies
Well-scoped individual prompts (~1100-2100 chars each)
No orchestrator reading between spawns 1-3 (fact-sheet shows 0 tool calls pre-spawn-4)
This is optimal parallel batching

Perspectives batch (agents 4-7):

Agents 4-7 (devils-advocate, simplifier, implementer, premortem) dispatched after agents 1-3 complete
Reviewing a concrete proposal synthesized from agent 1-3 results
All four perspectives agents dispatched together (lines 79, 81, 83, 85)
No reading between spawns 4-7
Pattern: Sequential synthesis (agents 1-3 complete → orchestrator synthesizes → agents 4-7 launch against synthesis)
This is reasonable but not pre-planned; it's reactive to agent results

Final deep-dives (agents 8-10):

Agent 8 (Explore Hermes agent OS): line 202
Agent 9 (Planka maturity deep-dive): line 218
Agent 10 (Modern kanban alternatives sweep): line 219
Agents 9-10 launched together but agent 8 is isolated
No orchestrator context prep visible between perspectives completion and agent 8 launch
Pattern: Reactive to ongoing synthesis, not pre-planned

Inefficiency candidate: The orchestrator could have planned agents 8-10 upfront rather than spawning them reactively. However, spawning them reactively against fresh synthesis might actually be more context-efficient (each agent sees the prior conclusions it's building on) — net verdict unclear.

Q4: Is the orchestrator avoiding reading files it does NOT need?

Verdict: PASS — Minimal unnecessary reading

File reading pattern:

Pre-spawn-1 through post-spawn-6: 0 bytes read (fact-sheet segments pre-spawn-1 through after-spawn-6)
After-spawn-7 (post-perspectives): 970 bytes read via Skill:1, Bash:1, Write:2
After-spawn-10 (final synthesis): 7106 bytes read via Bash:3, Read:1, Write:1, Edit:1

Details of after-spawn-7 reading (970 bytes):

Likely routine write of agent results or notes; Skill invocation suggests a vault operation

Details of after-spawn-10 reading (7106 bytes):

Line 239: Bash to list /home/jared/servers/ovh-prod/ and grep for SecondBrain references (practical investigation of user's infrastructure)
Line 244: Read tool (exact file unknown without parsing, but single Read suggests targeted lookup, not bulk scan)
Writes: likely capturing findings

Assessment: The orchestrator does not pre-load CLAUDE.md, docs/, or large context files before delegating. File operations are minimal and come after agents complete, suggesting the orchestrator is being selective about what to read. This is aligned with ORCHESTRATION.md guidance: "A short orienting Read before delegating is fine when the target file/path is uncertain. Don't delegate the orienting step itself."

Verdict: PASS — Prompts are focused, not bloated

Agent 1 prompt (Storybloq research): ~1098 chars

Specific research task (assess GitHub project for kanban/backlog viability)
Clear criteria (maturity, self-hosting, AI accessibility, visual dashboard)
No dump of CLAUDE.md, project history, or vault context
Appropriate scope for isolated research

Agents 2-3 prompts: ~1600-2100 chars each

Agent 2: Specific survey task (self-hosted kanban tools inventory)
Agent 3: Specific research task (agent-native/markdown backlogs)
No bloat; instruction-forward, not context-forward

Agents 4-7 (perspectives) prompts: ~2100-3300 chars each

Each perspective receives the concrete proposal being reviewed
No full CLAUDE.md dump; focused on the proposal and the perspective lens
Reasonable context load for judgment work

Agents 8-10 prompts: Unknown exact sizes but factsheet rows suggest ~1100-2300 chars (row 8: 1120 chars, row 9: 1832 chars, row 10: 2301 chars)

Proportionate to research scope, not bloated

Assessment: No evidence of context waste. Prompts are instruction-dense (telling agents what to do, why, and how to report) rather than context-dense (dumping large files).

Q6: Is the orchestrator following the ORCHESTRATION.md instructions?

Verdict: MIXED — Orchestrator tries to follow but Agent framework doesn't comply

ORCHESTRATION.md policy (stated verbatim):

"Do single-file, ≤2-tool-call ops directly. Don't delegate them."
"Delegate only when work is parallelizable across independent files/subtasks, spans many files, or needs a large/isolated context."
"Every Agent spawn passes model explicitly."
"Default haiku for mechanical file-edit/shell work; sonnet for anything requiring judgment; opus only for genuinely hard reasoning."

Orchestrator's behavior:

Single-file / ≤2-tool ops: Not violated. The orchestrator doesn't delegate write a single vault note or read a single file. Delegation is reserved for multi-sourced research and judgment work.
Parallelizable / spans-files: Respected. Agents 1-3 are independent research; agents 4-7 are parallel judgment.
Explicit model parameter: Orchestrator does this correctly — all 10 spawns include model: "sonnet". However, the Agent framework ignores the parameter and downgrades to Haiku. The orchestrator cannot be faulted for this; it's a framework/plugin issue.
Model selection per judgment: Orchestrator intends this correctly — research and perspectives are judgment work, so Sonnet is requested. But the framework downgrade means actual models are all Haiku.

Assessment: Orchestrator follows the spirit and letter of ORCHESTRATION.md. The failure (all Haiku instead of requested Sonnet) is a framework failure, not an orchestrator failure. However, the orchestrator could mitigate this if it detected the mismatch — it does not (no error handling for resolved model != requested model).

Refinement: The user's explicit delegation request ("Dispatch subagents...") overrides the ORCHESTRATION.md preference for direct work. This is correct per the policy's intent: "Delegate only when..." — the user's explicit need qualifies.

Q7: Is the orchestrator requesting/receiving back only the context it needs?

Verdict: PASS — Concise communication, no full-context-dump pattern

How agents report:

Agents complete asynchronously in the background (line 22: "The agent is working in the background. You will be notified automatically when it completes.")
Task notifications include agent output via the tool result (exact channel uncertain without parsing task output files)
Orchestrator receives task notifications as user lines, summarizes findings, and moves on

Orchestrator's synthesis pattern:

Lines 77-78: Orchestrator reads the Storybloq/tools/agent-native research and synthesizes key insights (adds recurrence/lifecycle concepts to the proposal) before perspectives
No evidence of the orchestrator reading 100KB of raw agent output; synthesis is rapid and concise

Final synthesis:

Line 201 shows orchestrator text output after perspectives complete, synthesizing four perspectives into recommendations
Tight, focused synthesis; no dumping of raw agent transcripts

Assessment: The communication pattern is clean. Agents produce output, orchestrator synthesizes selectively (line 77-78, 201) and moves forward. No evidence of "full context dump" or reading files it doesn't need.

Summary of Issues

Critical Issue

Model parameter not respected by Agent framework (lines 22, 24, 26, etc. — all resolvedModel = haiku despite model: sonnet request)

Blocks orchestrator from implementing cost-quality policy
Not an orchestrator error; framework/plugin failure
Orchestrator makes correct parameter choices; execution layer fails

Secondary Issue

Agents 8-10 appear reactive rather than pre-planned

Could indicate lack of forward planning
May be acceptable if reactive spawning is more context-efficient (each agent sees latest synthesis)
Weak signal without seeing the orchestrator's internal reasoning

N/A Issues

File reading: minimal and appropriate
Context sharing: focused, not bloated
Model selection intent: correct (all judgment work gets Sonnet request)
ORCHESTRATION.md compliance: followed except for framework failure

Recommendations

Immediate: Debug why Agent framework downgrades model parameter. Check Claude Code plugin / Agent framework configuration.
Mitigation: If framework issue is not fixable, orchestrator should handle resolved model != requested model (log warning, adjust expectations, or re-delegate to a higher tier).
Optional improvement: Pre-plan agents 8-10 upfront rather than spawning reactively, if the orchestrator can predict that deep-dives will be needed. Current reactive approach is defensible but less efficient than pre-batched delegation.
Documentation: Add a post-delegation checkpoint after agents 1-7 complete to confirm the proposal synthesis before spawning agents 4-7, making the sequential-reactive pattern explicit in the transcript (currently implied, not stated).

Checklist (7 Questions)

#	Question	Verdict	Key Evidence Line(s)
1	Subagents called when should be?	PASS	4, 20, 21 (user request + orchestrator accepts)
2	Correct model chosen?	FAIL	21/22, 23/24, 25/26 (sonnet requested, haiku resolved)
3	Tasks grouped efficiently?	MIXED	Lines 21-25 (good batch), 79-85 (reactive)
4	Avoiding unnecessary reads?	PASS	Factsheet: 0 reads pre-spawn-7
5	Too much context shared?	PASS	Prompts ~1-3KB each, instruction-forward not context-forward
6	Following ORCHESTRATION.md?	MIXED	Orchestrator complies; framework fails (model param ignored)
7	Getting back only needed context?	PASS	Concise synthesis, no full-dump pattern

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